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    <article id="post-NLP_BM25"  class="article article-type-post" itemscope itemprop="blogPost" >
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      <a class="article-title" href="/2020/01/07/NLP_BM25/">【NLP】相似度检索-BM25</a>
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	<a href="/2020/01/07/NLP_BM25/" class="article-date">
	  <time datetime="2020-01-07T02:39:58.970Z" itemprop="datePublished">2020-01-07</time>
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    <a class="article-category-link" href="/categories/NLP/">NLP</a>

      
      

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          概述BM25算法，通常用来作搜索相关性平分。一句话概况其主要思想：对Query进行语素解析，生成语素；然后，对于候选集D中的每个d，计算每个语素qi与d的相关性得分，最后，将qi相对于d的相关性得分进行加权求和，从而得到Query与d的相关性得分。
BM25一般公式
Score(Q,d)=\sum^{n}_{i} W_{i} \cdot R(q_{i},d)其中，$Q$表示Query，$q_i$表示$Q$解析之后的一个语素；

可以理解为对于$Q$进行分词后去停用词的内容List

$d$...
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    <article id="post-NLP_From_HMM_to_CRF"  class="article article-type-post" itemscope itemprop="blogPost" >
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      <a class="article-title" href="/2019/11/05/NLP_From_HMM_to_CRF/">【NLP】从隐马尔科夫到条件随机场</a>
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	<a href="/2019/11/05/NLP_From_HMM_to_CRF/" class="article-date">
	  <time datetime="2019-11-05T02:26:52.821Z" itemprop="datePublished">2019-11-05</time>
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    <a class="article-category-link" href="/categories/NLP/">NLP</a>

      
      

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在监督学习下，分类是非常经典的一类问题，也是在实际当中遇到的最多一类任务。
下面对于整个分类模型的相关知识点进行一个贯连，作为学习条件随机场（CRF）的前序导引知识。
笔记整理自：Bilibili站上shuhuai008强势手推讲解的白板推导CRF系列课程，课程质量很高！

前序知识串联对于解决分类任务的模型，我们可以将其划分为硬模型和软模型。

硬模型：即为输出即为确值（对于二分类就是0 or 1）

SVM（支持向量机）：最大支持向量的几何间隔驱动的；
PLA（感知机模型）：误分类驱...
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    <article id="post-Tools_zsh_command_not_found_tensorboard"  class="article article-type-post" itemscope itemprop="blogPost" >
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      <a class="article-title" href="/2019/11/02/Tools_zsh_command_not_found_tensorboard/">【工具篇】解决安装oh-my-zsh后出现command not found的问题</a>
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	<a href="/2019/11/02/Tools_zsh_command_not_found_tensorboard/" class="article-date">
	  <time datetime="2019-11-02T11:48:51.529Z" itemprop="datePublished">2019-11-02</time>
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    <a class="article-category-link" href="/categories/实用工具/">实用工具</a>

      
      

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          1、由于.zshrc 没有配置相关环境变量设置，需要将bash 中.bash_profile 全部环境变量加入
1open .zshrc
2、下拉找到# User configuration部分，在下面添加，并保存
1source ~/.bash_profile
3、返回到zsh下面，执行下面命令后，问题解决。
1source .zshrc

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    <article id="post-Tools_String_format"  class="article article-type-post" itemscope itemprop="blogPost" >
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      <a class="article-title" href="/2019/11/02/Tools_String_format/">【工具篇】Python格式字符串(string.format)</a>
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	<a href="/2019/11/02/Tools_String_format/" class="article-date">
	  <time datetime="2019-11-02T09:09:59.798Z" itemprop="datePublished">2019-11-02</time>
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原文：Python String Format
译者：youngsterxyf
转载自：http://blog.xiayf.cn/2013/01/26/python-string-format/

每次使用Python的格式字符串（string formatter）我都贼懵圈，并且我都没有认真地理解它们的文档，所以着手编写自己的格式字符串手册。
数字格式化下面的表格展示了使用Python的后起新秀str.format()格式化数字的多种方法，包含浮点数格式化与整数格式化示例。可使用 pr...
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    <article id="post-ML_19_Ask_Anwser"  class="article article-type-post" itemscope itemprop="blogPost" >
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      <a class="article-title" href="/2019/10/21/ML_19_Ask_Anwser/">【机器学习】灵魂19问</a>
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	<a href="/2019/10/21/ML_19_Ask_Anwser/" class="article-date">
	  <time datetime="2019-10-21T15:29:51.806Z" itemprop="datePublished">2019-10-21</time>
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    <a class="article-category-link" href="/categories/机器学习/">机器学习</a>

      
      

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          Q1:有哪些你熟悉的监督学习和无监督学习算法？
监督学习：
线性回归
逻辑回归
SVM（SVC &amp;&amp; SVR）
决策树
朴素贝叶斯
神经网络


无监督学习：
K-means
PCA



Q2:生成模型和判别模型的区别？
判别模型：  是直接根据现有的数据，通过数据集给定的X，学习到相应的决策函数，来预测Y；
生成模型：  是先学习到数据集上的联合概率分布情况，然后，再进一步从联合概率的来预测Y。

Q3:线性分类器与非线性分类器的区别以及优劣
线性分类器：通过线性函数来...
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      <a class="article-title" href="/2019/10/20/ML_Usage_of_sklearn/">【机器学习】动手实操(Sklearn)</a>
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	<a href="/2019/10/20/ML_Usage_of_sklearn/" class="article-date">
	  <time datetime="2019-10-20T10:22:50.175Z" itemprop="datePublished">2019-10-20</time>
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    <a class="article-category-link" href="/categories/机器学习/">机器学习</a>

      
      

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          一、ML的实操的灵魂4问Q1：请写出你了解的机器学习特征工程操作，以及它的意义【解答】：
特征工程是在拿到数据后，将数据进行观测处理，为机器学习算法提供数据，所构建的特征好坏决定着机器学习算法的天花板，所以，对于机器学习算法，特征工程是至关重要的。特征工程一般包括：

缺失值的删除、处理
Scaling幅度缩放：归一化（标准化）
连续值离散化
多项式处理
LabelEncoder/One-Hot变换
特征选择

Q2：请写出上述特征工程操作的sklearn或者pandas实现方式【解答】：...
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    <article id="post-NLP_WordCloud"  class="article article-type-post" itemscope itemprop="blogPost" >
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      <a class="article-title" href="/2019/10/06/NLP_WordCloud/">【NLP】WordCloud-词云</a>
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	<a href="/2019/10/06/NLP_WordCloud/" class="article-date">
	  <time datetime="2019-10-05T17:05:32.000Z" itemprop="datePublished">2019-10-06</time>
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文本的可视化工具，不像是matplotlib，对于数据又是可以折线图，条形图的，在NLP中，对于文本最常见的也就是词云。（个人感觉这个还不如直接看高频词表来的酸爽，词云的最大用处，我觉得就是酷炫，给枯燥的无味的PPT来加分，哈哈！）
(PS:当然，对于文本分类，查看各类中的分布情况用条形图是必须的。)

词云的食用方式
1.对数据集的文本进行分词（Jieba）
2.数据清洗（去停用词等）
3.词频统计（列出TopK的高频词）
4.将高频词表喂给WordCloud工具库，就可以成图啦。

...
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    <article id="post-NLP_language_recognizier_Base_NB"  class="article article-type-post" itemscope itemprop="blogPost" >
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      <a class="article-title" href="/2019/10/05/NLP_language_recognizier_Base_NB/">【NLP】用朴素贝叶斯完成语种识别器</a>
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	<a href="/2019/10/05/NLP_language_recognizier_Base_NB/" class="article-date">
	  <time datetime="2019-10-05T12:43:48.293Z" itemprop="datePublished">2019-10-05</time>
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    <a class="article-category-link" href="/categories/NLP/">NLP</a><a class="article-category-link" href="/categories/NLP/项目整理/">项目整理</a>

      
      

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          项目背景及主要内容
这个项目算是一个超简单的小例子，是基于机器学习[朴素贝叶斯]）的一个小项目，项目完成的任务类型为文本分类。
主要包含内容为：

0.用正则表达式，进行清洗数据（即删除信息中的无关内容，提高训练数据的纯度）
1.用BOW/TF-IDF，进行表征字符级的文本和n-gram；
2.用朴素贝叶斯算法，进行构建6个不同语种的分类模型；
3.用flask，进行项目的web部署。


数据介绍数据取自于Twitter，包含English, French, German, Spanis...
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      <a class="article-title" href="/2019/08/31/TF2_Tensorflow2_Keras/">【日月光华】TensorFlow2.0(一)--tf.keras</a>
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	<a href="/2019/08/31/TF2_Tensorflow2_Keras/" class="article-date">
	  <time datetime="2019-08-31T14:39:46.000Z" itemprop="datePublished">2019-08-31</time>
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    <a class="article-category-link" href="/categories/tensorflow/">tensorflow</a>

      
      

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本系列将整理我在Tensorflow2.0课程的学习笔记。
由于tf 1.0和tf 2.0的版本更新变化很大，所以，我就直接放弃1.0的学习啦，直接转战2.0了。
【课程】：在网易云课堂上的TF2.0的入门与实战
【授课人】：@日月光华


1.API—-tf.keras基础一般的用法分为5个步骤：

声明model为Sequential()模型
1model = tf.keras.Sequential()

给model从“输入”到“输出”按顺序添加各层（包括层的类型、激活函数、神经元...
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      <a class="article-title" href="/2019/08/27/Tools_Mac_zsh/">【工具篇】Mac终端美化：iterm2 + zsh + oh~my~zsh 设置教程</a>
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	  <time datetime="2019-08-27T14:37:12.382Z" itemprop="datePublished">2019-08-27</time>
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          mac自带的terminal终端没有文件名高亮等功能，而且界面不是很好看。既然让我知道了，不折腾怎么行！况且，还可以让自己使用起来更加方便，看起来更加舒服。
先上最后效果咯，见下图！

配置方法1.下载安装iterm2官方的下载地址为：https://www.iterm2.com/
这个就是一个app，安装的步骤和安装mac上其他应用是一样的，直接放到Application中就OK了。
为什么要用iterm2？因为原始的terminal不能美化呀，这个iterm2是可以美化的，懂了吧？
2...
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